Raise a better error if scikits.cuda isn't installed

theano/sandbox/cuda/fftconv.py

@@ -48,6 +48,12 @@ class ScikitsCudaOp(GpuOp):
 
         return theano.Apply(self, [inp], [self.output_type(inp)()])
 
+    def make_thunk(self, node, storage_map, _, _2):
+        if not scikits_cuda_available:
+            raise RuntimeError(
+                "scikits.cuda is needed for all GPU fft implementation,"
+                " including fftconv.")
+
 
 class CuFFTOp(ScikitsCudaOp):
     def output_type(self, inp):
@@ -56,6 +62,8 @@ class CuFFTOp(ScikitsCudaOp):
             broadcastable=[False] * (inp.type.ndim + 1))
 
     def make_thunk(self, node, storage_map, _, _2):
+        super(CuFFTOp, self).make_thunk(node, storage_map, _, _2)
+
         from theano.misc.pycuda_utils import to_gpuarray
         inputs = [storage_map[v] for v in node.inputs]
         outputs = [storage_map[v] for v in node.outputs]
@@ -111,6 +119,8 @@ class CuIFFTOp(ScikitsCudaOp):
             broadcastable=[False] * (inp.type.ndim - 1))
 
     def make_thunk(self, node, storage_map, _, _2):
+        super(CuIFFTOp, self).make_thunk(node, storage_map, _, _2)
+
         from theano.misc.pycuda_utils import to_gpuarray
         inputs = [storage_map[v] for v in node.inputs]
         outputs = [storage_map[v] for v in node.outputs]
@@ -300,6 +310,8 @@ class BatchedComplexDotOp(ScikitsCudaOp):
         return CudaNdarrayType(broadcastable=[False] * inp.type.ndim)
 
     def make_thunk(self, node, storage_map, _, _2):
+        super(BatchedComplexDotOp, self).make_thunk(node, storage_map, _, _2)
+
         inputs = [storage_map[v] for v in node.inputs]
         outputs = [storage_map[v] for v in node.outputs]